Method, apparatus and system for image acquisition and conversion

ABSTRACT

A method for image acquisition and conversion includes low-pass filtering an image by an acquisition lens, producing from the low-pass filtered image, an up-sampled image with a first resolution with an up-sampling factor using a image sensor. The up-sampled image is converted into a multi-level image with a second resolution lower than the first resolution with an image processing circuit. The converting depends on the low-pass filtering of the lens and on the up-sampling factor. The method is adapted to gigapixel sensors and convention image sensors.

REFERENCE DATA

This application claims priority of US2010/61296213, filed on Jan. 19, 2010 and of US2010/61302638, filed on Feb. 9, 2010, the contents whereof are hereby incorporated.

FIELD OF THE INVENTION

The present invention concerns a method for image acquisition and conversion. The present invention concerns also an image acquisition apparatus and a computer-program product.

DESCRIPTION OF RELATED ART

A simplified architecture of a camera or other imaging system is shown on FIG. 1 a. The camera comprises a lens 1 that focuses the incident light onto the plane of an image sensor 2 comprising a plurality of pixels 20 in a row or in an array. Each pixel 20 in the image sensor collects photons, and converts them to analog electrical signals, which are then quantized by a quantizer, such as an A/D converter, into 1, 8 or 12 bits for example. The number of photons hitting the pixel can be modeled by a Poisson process. The quantizers can be provided in each pixel or in a read-out circuit shared by a plurality of pixels.

The image sensor can be considered as a sampling device, which samples the integration function of the light intensity field during a given exposure time and over a space region. One of the most important parameter in a camera is the size of the pixels, which determines the spatial sampling interval.

Due to the diffraction limit, Rayleigh criterion gives the minimum spatial resolution of an ideal lens. The impulse response of the lens 1 is a point spread function (PSF), P(x) shown in FIG. 2. Because the lens has aberrations, and because there is diffraction even with an ideal lens, the point spread function is not a Dirac delta function; instead, the lens acts as a low-pass filter with a minimum spatial resolution larger than zero.

A model of the lens is shown as example in FIG. 3. The light intensity field before the lens is λ(x), where x denotes space index. Due to the low-pass effect of the lens, the light intensity field after the lens is bandlimited and equal to λ(x)*P(x), (* being the convolution operator).

Thanks to the scaling effects in CMOS technology, the state of the art pixel size used in consumer camera and other imaging equipments is often smaller than the minimum spatial resolution of the lens. In this case, the image sensor acts as an oversampling device and produces more samples than what would be required by the bandwidth of the image signal after bandlimitation by the lens.

Moreover, so-called gigapixel camera (or gigapixel digital film) are also known in which a massive oversampling factor is used. The image sensor of gigapixel camera have a non-linear response, similar to a logarithmic function, which make them very suitable for acquiring high dynamic range scenes. Moreover, gigapixel cameras usually have a single photon detector at each pixel, which reduce exposure time in low light conditions, due to the highly sensitive photon detection mechanism. Gigavision cameras can be used for taking videos or photos or, in general, any kind of image, including for example medical images using X-rays or other wavelengths instead of light.

Usually, the pixels in a gigapixel camera have a I-bit quantizer for converting the output of each pixel into binary values (black or white). The image output by the gigapixel sensor thus has a very high spatial resolution but only 2 levels at each pixels, for example only two grey levels. Therefore, a processing circuit is required in order to convert the high resolution, binary output of the gigapixel sensor into an image signal with a lower resolution but with more grey levels.

According to one aspect, the present invention thus relates to image conversion, in particular to methods for the reconstruction of an estimate of a multilevel image based on an oversampled digital signal. In some embodiments, the present invention relates to the conversion of a high resolution binary image signal into a lower resolution multilevel signals. In another embodiments, the present invention also relates to the conversion of a multilevel image signal into another signal with a different number of levels at each or some pixels, and/or with a different resolution. All embodiments can be used for the conversion of 1D, 2D or N-D image signals output by any image sensor.

FIG. 4 illustrates schematically an image sensor 2. The effect of this sensor is to sample the incoming light that passes through the lens 1. The number of electrons S_(k) generated by the kth pixel depends on the number of photons impinging on this pixel. For example, if the quantum efficiency of the pixel is ‘1’, the number of electrons generated by a pixel 20 is equal to the number of photons received by this pixel.

During an exposure time τ, and pixel width Δx, the number of electrons S_(k) generated by the kth pixel obeys a Poisson distribution with parameters {tilde over (λ)}_(k) which is the average number of electrons generated by the pixel during the exposure time. This distribution can be written as:

${P\left\lbrack {S_{k} = i} \right\rbrack} = {^{- {\overset{\sim}{\lambda}}_{k}}\frac{{\overset{\sim}{\lambda}}_{k}^{i}}{i!}}$

As illustrated on FIG. 4, the value delivered by the image sensor 2 is a convolution of the signal after the lens λ(x)*P(x) with a kernel function ƒ(x), followed by a sampling of the resulting function {tilde over (λ)}(x) at

$x = {\frac{x_{k - 1} + x_{k}}{2}.}$

A quantizer is then used to quantize the number of electrons and produces a pixel value B_(k) which is also a random variable:

$\begin{matrix} \left\{ \begin{matrix} {{P\left\lbrack {B_{k} = 0} \right\rbrack} = {\sum\limits_{i = 0}^{Q_{1} - 1}{^{- {\overset{\sim}{\lambda}}_{k}}\frac{{\overset{\sim}{\lambda}}_{k}^{i}}{i!}}}} \\ {{P\left\lbrack {B_{k} = 1} \right\rbrack} = {\sum\limits_{i = Q_{1}}^{Q_{2} - 1}{^{- {\overset{\sim}{\lambda}}_{k}}\frac{{\overset{\sim}{\lambda}}_{k}^{i}}{i!}}}} \\ \ldots \\ {{{P\left\lbrack {B_{k} = {L - 1}} \right\rbrack} = {\sum\limits_{i = Q_{L - 1}}^{+ \infty}{^{- {\overset{\sim}{\lambda}}_{k}}\frac{{\overset{\sim}{\lambda}}_{k}^{i}}{i!}}}},} \end{matrix} \right. & \; \\ {{k = 1},2,\ldots \mspace{14mu},{K.}} & \; \end{matrix}$

The starting position of the k_(th) pixel is x_(k−1), and the ending position is x_(k). Therefore, the estimation {tilde over (λ)}_(k) of the light received by the pixel is:

${{\overset{\sim}{\lambda}}_{k} = {{\int_{x_{k - 1}}^{x_{k}}{\tau \; {\lambda (x)}*{P(x)}{x}}} = {\tau \; {\lambda (x)}*{P(x)}*{f(x)}*{\delta \left( {x - \frac{x_{k - 1} + x_{k}}{2}} \right)}}}},$

After quantization, the image sensor 2 produces a set of numerical values B=[B₁, B₂, . . . , B_(K)]^(T), b=[b₁, b₂, . . . , b_(K)]^(T) is a realization of the random variable B.

One aim of the present invention is thus to produce an estimate of the light intensity field {tilde over (λ)}(x), given those quantized pixel value.

In particular, it is an aim of the present invention to retrieve the light intensity field {tilde over (λ)}(x) based on the output of an oversampling image sensor, i.e., an image sensor with a spatial and/or temporal frequency larger than the Nyquist rate of {tilde over (λ)}(x)

In the special case of a gigapixel sensor, an aim of the invention is to reconstruct a conventional image (with grey levels) using the binary, higher resolution image output by the sensor.

Reconstruction methods for reconstructing an image from quantized measurements taken by an oversampling camera, such as a gigapixel camera, are known in the prior art. For example, it is known to add or average signals delivered by adjacent pixels in blocks. In the case of a gigapixel camera, it has been suggested for example to produce a multivalued signal that indicates grey levels by summing the binary values B within blocks of adjacent pixels. Other methods based on a low-pass filtering and downsampling the output of the image sensor are also known. This processing is usually done in the readout circuit of the image sensor, or could be done by any digital image processing system in the camera or in a computer or other processing system that receives and process the captured images.

A problem of this prior art approach is that the performance of reconstructing images by low-pass filtering and down-sampling is not good, or at least not optimal.

One difficulty in prior art image sensor is to determine the thresholds Q₁ used by the quantizer to distinguish between levels. For example, when the light intensity is high, a gigapixel camera with a large threshold Q₁ works better than one with a small threshold. When the light intensity is small, a gigavision camera with a small threshold Q₁ works better than one with a large threshold. The same difficulties apply also with image sensors producing multilevel images.

Therefore, there is a need for an improved image acquisition and reconstruction method and apparatus that deliver a better quality of image and that produce a better estimate of the incoming light field based on quantized values generated by the image sensor.

There is also a need to improve the speed of the reconstruction algorithm for reconstructing an image from quantized measurements taken by the camera.

There is also a need for a reconstruction method where the computing complexity does not significantly increase when an image is captured under multiple exposures or with massive spatial oversampling.

BRIEF SUMMARY OF THE INVENTION

The reconstruction method and apparatus of the invention is based in part on novel uses of the sampling theory. This theory tells us that we can perfectly reconstruct an estimated light intensity field {tilde over (λ)}(x) from samples γ_(j), j=1, 2, . . . , J at Nyquist rate:

${{\overset{\sim}{\lambda}(x)} = {\sum\limits_{j = 1}^{J}{\gamma_{j}{\varphi\left( {x - x_{j}}\; \right)}}}},$

where γ_(j), j=1, 2, . . . , J are samples of the estimated light intensity field {tilde over (λ)}(x) at Nyquist rate, φ(x) is a kernel function, and x_(j) is the sampling position for the jth pixel. The oversampling rate is

$N = {\frac{K}{J}.}$

Taking into account the sampling position x_(k), k=1, 2, . . . , K, the previous expression can be written in digital form as:

${\overset{\sim}{\lambda}}_{k} \approx {\sum\limits_{j = 1}^{J}{\gamma_{j}{{\varphi_{N}\left( {x_{k} - x_{j}} \right)}.}}}$

We use the symbol “≈” in the above equation since the kernel function is often not the ideal one, but only an approximation, based on the assumption that when the pixel size Δx is changed by a factor N, the kernel function needs only to be changed by this scaling factor.

Therefore, according to an aspect of the invention, the above mentioned aims are achieved by means of a method where the conversion of the image signal is not just a series of low-pass filtering and downsampling operations as in the prior art; instead, this conversion is considered as a reconstruction of a signal from digital samples values, for example reconstruction of a signal from digital values taken at a sampling rate above the Nyquist rate, and taking into account properties of the whole optical and electronic system, in particular properties of the lens.

The above mentioned aims are also achieved by means of a method for image acquisition and conversion comprising

-   -   low-pass filtering (or more generally transforming with a         transfer function) an image by an acquisition lens,     -   producing from said low-pass filtered image an up-sampled image         with a first resolution with an up-sampling factor using a image         sensor,     -   converting said up-sampled image into a multi-level image with a         second resolution lower than said first resolution with an image         processing circuit

wherein said converting step depends on said low-pass filtering of said lens and on said up-sampling factor.

The reconstruction depends on the transfer function of the lens and on the oversampling factor, thus resulting in an optimization of the whole system and in an improved image quality.

The output delivered by the reconstruction process is typically a multi-level image, i.e., an image with more than two different possible values at each pixel, such as a grayscale or color image.

In one embodiment, the up-sampled image is produced with a gigapixel image sensor that outputs a binary up-sampled image.

In one embodiment, the sensor quantizer comprises a spatially-varying arrangement. For example, in the 1-bit quantizer case, one group of pixels on the sensor could have a threshold Q₁=1, and the rest of the pixels a higher threshold, or various different thresholds.

Advantageously, the conversion into a multi-level image is done with a method and circuit that implements a maximum likelihood estimation method.

According to one aspect, the invention is based on the finding that the negative log-likelihood function is a convex function; this is true in particular when the threshold Q of the quantizer is “1”, but also when the threshold is different from “1”, and with quantizers having a plurality of thresholds for producing multilevel measurement values B. Therefore, optimal solution can be achieved using convex optimization.

In one embodiment, at least two exposures are provided for each image acquired, thus resulting in temporal oversampling and in even more samples used for each image. Methods are described that allow using multiple exposure without substantial increase of the computational complexity.

According to an aspect, the reconstruction method uses a maximum likelihood method based on filter bank techniques for computing the gradient and the multiplication of a vector and Hessian matrix of the negative log-likelihood function. The use of filter bank techniques results in fast processing.

Advantageously, a polyphase representation or another improved representation of the signals and operators is used in order to increase the computing speed.

An important advantage of the method according to the invention is to allow a reconstruction of an image exploiting and depending on the low-pass filtering function of the lens and the up-sampling function of the gigavision sensor.

Advantageously, the reconstruction of the image from the measurements output by the sensor can be done by DSP elements, by FPGA components, by a microprocessor or microcontroller inside a camera or in a computer. For example, the maximum likelihood estimation used within the method can be carried out by means among this list, or by any other suitable hardware or software means.

For this purpose, a program of image elaboration during the treatment of image takes into account the sensor and the lens of the camera. In another embodiment, the reconstruction is done by an image processing apparatus outside of the camera, for example by an image processing software executed by a computer or IT equipment that receives and processes image files output by the camera. The invention also relates to a computer program product that tangibly and permanently stores a computer program for causing a processing system to perform the method described in this application.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood with the aid of the description of an embodiment given by way of example and illustrated by the Figures, in which:

FIG. 1 a shows a simplified architecture of a camera. The incident light is focused by the lens and then impinges on the image sensor. After that, each pixel in the image sensor converts the received photons to electrons.

FIG. 1 b shows a simplified architecture of an image acquisition apparatus comprising a camera and additional image processing means.

FIG. 2 shows an example of point spread function of the lens.

FIG. 3 shows the model of the lens. The lens is modeled as a linear system with impulse response P(x). The result of a light intensity field λ(x) passing through the lens is λ(x)*P(x).

FIG. 4 shows a model of the image sensor using an L-level quantizer with levels, Q₁, Q₂, . . . , Q_(L−1) to quantize the number of electrons and to produce the pixel values B_(k), k=1, 2, . . . , K.

FIG. 5 shows a example of model for the camera of the invention. γ=[γ₁, γ₂, . . . , γ_(J)]^(T) are the samples of {tilde over (λ)}(x) at Nyquist rate. N is upsampling factor. g[n] is a low-pass filter. {tilde over (λ)}=[{tilde over (λ)}₁, {tilde over (λ)}₂, . . . , {tilde over (λ)}_(K)]^(T) are oversampled samples of {tilde over (λ)}(x). B=[B₁, B₂, . . . , B_(K)]^(T) are the quantized pixel values, b=[b₁, b₂, . . . , b_(K)]^(T) is a realization of B. {circumflex over (γ)}=[{circumflex over (γ)}₁, {circumflex over (γ)}₂, . . . , {circumflex over (γ)}_(J)]^(T) are the reconstructed values of {tilde over (λ)}(x).

FIG. 6 shows the upsampling and low-pass filtering operator G.

FIG. 7 shows the operator G^(T) for low-pass filtering and downsampling.

FIG. 8 shows a diagram from computing L(γ)'s Hessian matrix Hv times a vector v. g[n] and g[−n] are low-pass filters.

FIG. 9 shows the polyphase representation of a sequence x[k] (left) and a filter g[k] (right).

FIG. 10 shows an example of a 1-D synthesized signal γ.

FIG. 11 shows an example of an upsampled and low-pass filtered signal {tilde over (λ)} using an oversampling factor N=100, and an exposure time τ=1.

FIG. 12 shows an example of a binary sequence generated by the camera with threshold Q₁=1, an oversampling factor N=100, and totally exposure time τ=1.

FIG. 13 shows an example of a 1-D synthesized signal γ, as well as the estimated signal {circumflex over (γ)}, using a threshold a Q₁=1, an oversampling factor N=100, a number of exposures M=1, and an exposure time τ=1.

FIG. 14 shows an example of a 1-D synthesized signal y, as well as the estimated signal {circumflex over (γ)}, using a threshold Q₁=1, an oversampling factor N=20000, a number of exposures M=1 and an exposure time τ=1.

In another embodiment, the quantizer is an L-level quantizer 4 with a plurality of levels Q₁, Q₂, . . . , Q_(L−1); the thresholds between the levels can be equidistant or not, and the quantized produces multilevel pixel values B_(k), k=1, 2, . . . , K indicating level of grays for each of the K pixels. As will be described later, different quantizers with different number of thresholds, and/or with different distances between the thresholds, can be associated with one single image sensor.

Reference 23 on FIG. 1 b shows a processing circuit for processing the quantized pixel value B delivered by the quantizer(s) 22. The processing circuit may be built around a microprocessor, a microcontroller, a digital processing circuit, a FPGA, an asic, etc. The processing circuit is connected to a memory, for example a semi permanent memory 25 such as a Flash memory for example, for storing software executed by the processing circuit (bios) and processed images delivered by the processing circuit 23. The processing circuit executes program or functions for converting the image signals delivered by the image sensor 22 into lower-resolution image files, using reconstruction methods described below. Those reconstruction methods can also be executed, entirely or in part, by programs executed outside of the camera, for example in a personal computer 7, workstation, server or any other IT system able to receive and process oversample samples produced by the image sensor.

Since the reconstruction depends on the transfer function of the lens and on the oversampling factor used by the image sensor, those parameter need to be known by the processing circuit 23 that perform the reconstruction. If the lens is not integral with this processing circuit, the type of lens which is used, or its transfer function, can be indicated as metadata in the image file, or input by the user, or retrieved from a knowledge database for instance or a priori known. The oversampling factor can usually be retrieved from the image files, for example by determining the number of samples b.

FIG. 5 is another block diagram of the apparatus of the invention. The light intensity field γ=[γ₁, γ₂, . . . , γ_(J)]^(T) is spatially (and/or temporally) upsampled by a factor N by the camera sensor 2 and filtered by a low-pass filter g[n] corresponding to the lens 1, thus producing oversampled samples of {tilde over (λ)}(x), {tilde over (λ)}=[{tilde over (λ)}₁, {tilde over (λ)}₂, . . . , {tilde over (λ)}_(K)]^(T). The relation between the light intensity field {tilde over (λ)} at the output of the image sensor and the light intensity field γ before the lens can be written as {tilde over (λ)}=Gγ, where G=[g₁, g₂, . . . , g_(K)]^(T) is a K×J matrix representing the upsampling and low-pass filtering operator, as shown in FIG. 6.

A quantized pixel value B is then generated by the quantizers 22. Finally a reconstruction algorithm is executed by digital processing means 23 in the camera or in a digital processing system to obtain an estimate of the light intensity field {circumflex over (γ)}=[{circumflex over (γ)}₁, {circumflex over (γ)}₂, . . . , {circumflex over (γ)}_(J)]^(T).

The aim of the reconstruction is to compute the estimate {circumflex over (γ)} of the light intensity field. According to one aspect of the invention, the reconstruction method carried out by the digital processing means 23 uses a maximum likelihood estimator (MLE), or other maximum likelihood estimating means, for solving the reconstruction problem and for computing {circumflex over (γ)}:

$\begin{matrix} {\hat{\gamma} = {\underset{\gamma}{\arg \; \min}\; {P\left( {B;\gamma} \right)}}} \\ {\overset{(1)}{=}{\underset{\gamma}{\arg \; \min}{\prod\limits_{k = 1}^{K}{\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{k} - l} \right)}{P\left( {{B_{k} = l},{\overset{\sim}{\lambda}}_{k}} \right)}}}}}} \\ {\overset{(2)}{=}{\underset{\gamma}{\arg \; \min}\; {\ln \left( {\prod\limits_{k = 1}^{K}{\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{k} - l} \right)}{P\left( {{B_{k} = l};{\overset{\sim}{\lambda}}_{k}} \right)}}}} \right)}}} \\ {\overset{(3)}{=}{\underset{\gamma}{\arg \; \min} - {\sum\limits_{k = 1}^{K}{\ln \left( {\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{k} - 1} \right)}{P\left( {{B_{k} = l};{\overset{\sim}{\lambda}}_{k}} \right)}}} \right)}}}} \\ {= {\underset{\gamma}{\arg \; \min}{\mathcal{L}(\gamma)}}} \end{matrix}$

Those relations are based on the following findings: in (1), we use the independence of each pixel when given γ_(k), in (2) we use the fact that the logarithm function ln does not change the solution of a maximization problem, and in (3) we use the property that the solution of maximizing a function is equal to minimize the corresponding negative function.

For each pixel k, the negative log-likelihood function L(γ) can be written as

${{L_{k}\; (\gamma)} = {- {\ln \left( {\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{k} - l} \right)}{P\left( {{B_{k} = l};{\overset{\sim}{\lambda}}_{k}} \right)}}} \right)}}},{{{then}\mspace{14mu} {L(\gamma)}} = {\sum\limits_{k = 1}^{K}{{L_{k}(\gamma)}.}}}$

It can be demonstrated that L(γ) is a convex function, even if the quantizer is a multi-level quantizer. A demonstration can be found in the Appendix.

In addition to or instead of the spatial oversampling, it is also possible to perform a temporal oversampling by taking M pictures during each time period [0,τ]. In this example, all pictures are taken with the same exposure time

$\frac{\tau}{M};$

different exposure times can also be considered.

Let {tilde over (λ)}(x) be the new parameter function for the image sensor, then

${{\overset{\overset{\sim}{\sim}}{\lambda}(x)} = {\frac{\overset{\sim}{\lambda}(x)}{M} = {\frac{\tau}{M}{\lambda (x)}*{P(x)}*{f(x)}}}},$

which is the same for each exposure. So we scale down the original parameter function by a factor of M. Then

${\overset{\sim}{\gamma} = \frac{\gamma}{M}},{{{and}\mspace{14mu} \overset{\overset{\sim}{\sim}}{\lambda}} = {\left\lbrack {{\overset{\overset{\sim}{\sim}}{\lambda}}_{1},{\overset{\overset{\sim}{\sim}}{\lambda}}_{2},\ldots \mspace{14mu},{\overset{\overset{\sim}{\sim}}{\lambda}}_{k}} \right\rbrack^{T} = {\frac{\overset{\sim}{\lambda}}{M}.}}}$

Let {tilde over (B)}[B₁, B₂, . . . , B_(M)], where B_(m)=[B_(m1), B_(m2), . . . , B_(mK)]^(T), m=1, 2, . . . , M is the output pixel values during m th exposure, and B_(mk), k=1, 2, . . . , K is the pixel value of k th pixel, during the m^(th) exposure. The maximum likelihood estimator (MLE) to estimate γ is

$\begin{matrix} {\hat{\gamma} = {\underset{\gamma}{\arg \; \max}{{\mathbb{P}}\left( {\overset{\sim}{B};\gamma} \right)}}} \\ {\overset{(1)}{=}{\underset{\gamma}{\arg \; \max}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{{\mathbb{P}}\left( {{B_{mk} = l};{\overset{\overset{\sim}{\sim}}{\lambda}}_{k}} \right)}}}}}}} \\ {= {\underset{\gamma}{\arg \; \max}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{{\mathbb{P}}\left( {{B_{mk} = l};\frac{{\overset{\sim}{\lambda}}_{k}}{M}} \right)}}}}}}} \\ {\overset{(2)}{=}{\underset{\gamma}{\arg \; \max}{\ln \left( {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{{\mathbb{P}}\left( {{B_{mk} = l};\frac{{\overset{\sim}{\lambda}}_{k}}{M}} \right)}}}}} \right)}}} \\ {{\overset{(3)}{=}{\underset{\gamma}{\arg \; \min} - {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\ln \left( {\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{{\mathbb{P}}\left( {{B_{mk} = l};\frac{{\overset{\sim}{\lambda}}_{k}}{M}} \right)}}} \right)}}}}},} \end{matrix}$

The estimator thus uses the following findings: in (1), the relation is based on the independence of each pixel when given λ_(k), in (2) the estimator uses the fact that ln does not change the solution of a maximization problem, and in (3) the estimator uses use the property that the solution of maximizing a function is equal to minimize the corresponding negative function.

Thus, according to previous equation,

${\hat{\gamma} = {\underset{\gamma}{\arg \; \min}{\overset{\sim}{L}(\gamma)}}},$

where L⁻(γ) is the negative log-likelihood function,

${{\overset{\sim}{L}(\gamma)} = {- {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\ln \left( {\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{P\left( {B_{mk} = {l:\frac{{\overset{\sim}{\lambda}}_{k}}{M}}} \right)}}} \right)}}}}},{\gamma \in {R_{+}^{J}.}}$

${{{Let}\mspace{14mu} {{\overset{\sim}{L}}_{mk}(\gamma)}} = {- {\ln \left( {\sum\limits_{l = 0}^{L - 1}{{\delta \left( {B_{mk} - l} \right)}{P\left( {{B_{mk} = l};\frac{{\overset{\sim}{\lambda}}_{k}}{M}} \right)}}} \right)}}},{{{then}\mspace{14mu} {\overset{\sim}{L}(\gamma)}} = {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{{\overset{\sim}{L}}_{mk}(\gamma)}.}}}}$

In multiple successive exposures, as in the case of single exposure, it can be demonstrated that L⁻(γ) is a convex function. (see Appendix). Therefore, since the negative log-likelihood function is a convex function, the estimator can use any method for solving a convex optimization problem, like interior-point method, trust-region method, or Newton's method for example. Advantageously, the method is selected so as to reduce the computing time and power required to find the solution.

In one embodiment, the estimator uses the following modified Newton's method for estimating the incoming light in the one exposure case. The one skilled in the art can adapt this method to the multiple exposures case. A possible pseudo codes is as follows:

Pseudocode for Modified Newton's method given a starting point γ₀ ε domL(γ), domL(γ) is the domain of function L(γ), tolerance ε = 10⁻⁵. repeat 1. Compute the Newton step and decrement.  Calculate ∇L(γ), quit if ||∇L(γ)|| · ε  Δγ := −∇²L(γ)⁻¹∇L(γ);  θ² := −∇L (γ)^(γ) Δγ, quit if θ²/2 · ε. 2. Line search.  Choose step size φ by backtracking line search.  given a descent direction Δγ for L(γ)  α = 0.25, β = 0.5, φ := 1,  while γ + φΔγ ∉ domL(γ),   if φ < 10⁻⁵, find position set I,    that γ_(i) ≧ γ_(min) or γ_(i) · γ_(max), i ε I     if I is not empty, Δγ_(j) = 0, j ε J − I      where J = {1,2, . . . ,J}.     else quit.   else φ := βφ  while L(γ + φΔγ) > L(γ) + αφ∇LΔγ, φ := βφ 3. Update. γ := γ + φΔγ.

A problem is that the size of the Hessian matrix ∇²L(γ) is very large, so that computing the inverse of this matrix requires a lot of processing power and processing time. According to one aspect, in order to reduce this computing time, the system is programmed so as to perform a conjugate gradients method and directly compute ∇²L(γ)⁻¹∇L(γ).

In most known methods for solving convex optimization problem, like the Newton's method, the interior-point method or the trust-region method, it is needed to provide the negative log-likelihood function's gradient, and the Hessian matrix multiplication with a vector.

The method used in the method of the invention is also based on the finding that the gradient of the negative log-likelihood function

${{{L(\gamma)}\mspace{14mu} {is}\mspace{14mu} {\nabla{L(\gamma)}}} = {G^{T}\left\lbrack {\frac{{\partial L_{1}}\; (\gamma)}{\partial{\overset{\sim}{\lambda}}_{1}},\frac{\partial{L_{2}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{2}},\ldots \mspace{14mu},\frac{\partial{L_{K}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{K}}} \right\rbrack}^{T}},$

and the Hessian matrix of L(γ) is H=G^(T) AG, where,

$A = {\begin{pmatrix} \frac{\partial^{2}{\mathcal{L}_{1}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{1}} & 0 & \ldots & 0 \\ 0 & \frac{\partial^{2}{\mathcal{L}_{2}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{2}} & \ldots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots & \frac{\partial^{2}{\mathcal{L}_{\kappa}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{\kappa}} \end{pmatrix}.}$

A problem with the above two equations is that if the upsampling factor is large, the matrix G is big and huge storage space would be required to store it.

However, the operator G corresponds to the lens and to the upsampling part of the image sensor, and there is no requirement for storing the whole matrix. We only need to know the parameters of the upsampling and low-pass filtering operator, i.e., the upsampling factor N and the coefficients of the low-pass filter g[n]. The coefficients of the low-pass filter of the tens can be stored in a storage of the lens, and transmitted to the processing means that make the transformation. In another embodiment, those coefficients are a priori known by the processing means, for example if the processing means know in advance the properties of the lens that is used. In yet another embodiment, those properties are retrieved by the processing means based on an identification of the type of lens received by the processing means.

We can write G=LU, where L indicates low-pass filtering operator and U denotes upsampling operator. Then G^(T)=(LU)^(T)=U^(T) L^(T)=DR, where D is the matrix notation for downsampling operator and R is the matrix notation for low-pass filtering operator. The downsampling factor of D is equal to the upsampling factor of U. If the filter coefficients of L is g[n], then the filter coefficient of R is g[−n]. In the case of symmetric low-pass filter, the filter coefficients of L and R are the same. FIG. 7 illustrates the matrix for low-pass filtering and downsampling operator G^(T)

The gradient of L(γ) can be computed by first low-pass filtering the vector

$\left\lbrack {\frac{\partial{L_{1}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{1}},\frac{\partial{L_{2}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{2}},\ldots \mspace{14mu},\frac{\partial{L_{K}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{K}}} \right\rbrack^{T},$

then downsampling it by the factor N.

The negative log-likelihood function's Hessian matrix H times a vector v is Hv=G^(T) AGv. FIG. 8 shows the diagram for computing the above equation. The vector v is upsampled with upsampling factor N, then low-pass filter it using a filter g[n].

Since the matrix A is a diagonal matrix, the multiplication of A and vector Gv is equal to the elementwise multiplication of the diagonal of A and Gv. After that, the processing eans low-pass filter the obtained vector with a filter g[−n] and downsample by a factor of N to get Hv.

Therefore, the processing means can reconstruct the image signal from the measured samples.

According to one aspect of the invention, in order to further increase the speed of the optimization process, a polyphase representation can be used to reduce the computing time of the upsampling and low-pass filtering operator. Different polyphase representations can be defined for a sequence and for a filter.

A 1-D sequence x[k] or filter g[k] can be decomposed into N polyphase components, defined as,

${{x_{i}\lbrack k\rbrack}\overset{\Delta}{=}{x\left\lbrack {{Nk} + i} \right\rbrack}},{{g_{i}\lbrack k\rbrack}\overset{\Delta}{=}{g\left\lbrack {{Nk} - i} \right\rbrack}},{i = 0},1,\ldots \mspace{14mu},{N\; {{¡1}.}}$

FIG. 9 schematically illustrates a polyphase representation of a sequence x[k] (left part) and of a filter g[k] (right part). We can also compute this polyphase representation in the z-domain:

${{X(z)} = {\sum\limits_{n = 0}^{N - 1}{z^{- n}{X_{n}\left( z^{N} \right)}}}},{{{and}\mspace{14mu} {G(z)}} = {\sum\limits_{n = 0}^{N - 1}{z^{n}{{G_{n}\left( z^{N} \right)}.}}}}$

Let Y(z) be the z transform of the sequence y[k], which is the output when we implement the low-pass filtering and downsampling operator on a signal x[k], then we have

${Y(z)} = {\sum\limits_{n = 0}^{N - 1}{{H_{n}(z)}{{X_{n}(z)}.}}}$

This means that the process for the implementation of the operator on a sequence x[k] is decomposing the filter and sequence into N polyphase components, filtering the nth polyphase component of the sequence separately with the corresponding nth polyphase component of the filter, and summing all the filtered results to generate y[k].

During this process, the processing means avoid to compute the sequence value which will be discarded during the downsampling process, therefore the computing time can be saved.

If g[k] is decomposed using the definition of polyphase representation for a sequence, then

${G(z)} = {\sum\limits_{n = 0}^{N - 1}{z^{- n}{H_{n}\left( z^{N} \right)}}}$

and the output of the implementing the upsampling and low-pass filtering operator on x[k] can be written as

${\begin{pmatrix} {Y_{0}(z)} \\ {Y_{1}(z)} \\ \vdots \\ {Y_{N}(z)} \end{pmatrix} = {\begin{pmatrix} {G_{0}(z)} \\ {G_{1}(z)} \\ \vdots \\ {G_{N}(z)} \end{pmatrix}{X(z)}}},$

where Y_(n)(z) is the z transform of the nth polyphase component of the output sequence y[k].

Using this method, the processing means avoid to compute the multiplication of the filter coefficient and the “0”s generated during the upsampling process, and thus increase the speed by a factor of N.

Analysis have also shown that in the 1-bit quantizer case, a lower threshold Q₁ leads to small estimation errors in low light situations, but does not perform well in high light intensity regions, for which a higher threshold is more suitable.

To address the problem of a suitable threshold, in one embodiment the sensor quantizer has a spatially-varying arrangement. For instance, in one embodiment using a 1-bit quantizer, a first group of pixel on the sensor has a threshold Q₁=1, while other have a higher threshold. Similarly, in embodiment having n-bits quantizers, a plurality of different thresholds can be used for various pixels of the same chip. It is also possible to vary the threshold in time, or depending on lighting conditions.

The two types of pixels can be spatially-interlaced according to a given pattern. It can be shown that in this case, the negative log-likelihood function is still a convex function. All the previous techniques can thus be used.

The arrangement of different types of pixels (with different thresholds) on a given sensor can be designed to achieve an optimal pattern and arrangements of different threshold values, achieving best signal-to-noise ratio.

In order to design an optimal pattern, the following method can be used. If the total number of pixels is N, the maximum threshold value Q_(max), and aε[0,a_(max)]. In an example, the pixels only have two different thresholds Q₁₁, and Q₁₂, N₁ be the number of pixels have threshold Q₁₁, and N₂=N−N₁ be the number of pixels have threshold Q₁₂. Then the problem is what is the optimal Q₁₁, Q₁₂, N₁, N₂ that can maximize arg_(a)min SNR_(max) (Q₁₁, Q₁₂, N₁, N₂)? This is equal to solve the problem:

$Q_{11},Q_{12},N_{1},{N_{2} = {\underset{Q_{11},Q_{12},N_{1},N_{2}}{\arg \; \max}\underset{a}{\arg \; \min}{{SNR}_{\max}\left( {Q_{11},Q_{12},N_{1},N_{2}} \right)}}}$ s.t. aε[0,a _(max) ],Q ₁₁ ,Q ₁₂ε[1,Q _(max) ],N ₁ ,N ₂ε[0,N],N ₁ +N ₂ =N,

where

${{SNR}_{\max}\left( {Q_{11},Q_{12},N_{1},N_{2}} \right)} = {a{\sqrt{\frac{R_{Q_{1}}^{2}}{N_{1}{{MP}_{Q_{1}}\left( {^{\frac{a}{N_{1}M}} - P_{Q_{1}}} \right)}} + \frac{R_{Q_{2}}^{2}}{N_{2}{{MP}_{Q_{2}}\left( {^{\frac{a}{N_{2}M}} - P_{Q_{2}}} \right)}}}.}}$

Since this optimal pattern design is only done once, during design of a sensor chip, and the variables range is not large, exhaustive search method can be used to solve this optimization problem.

Here we only consider the case that we have two thresholds, more complex cases that have multiple thresholds, or multi-level quantizer can also be done in the same way.

A simple example is given. When a_(max)=100, Q_(max)=9, and N=100, M=1. Using the above algorithm, the optimal pattern is that N₁=37 pixels have Q₁₁=1, and N₂=63 pixels have Q₁₂=9.

Experimental results for 1D and for 2D images have shown that increasing the spatial and/or temporal oversampling factor increase the performance of the apparatus and method when the threshold Q₁=1. When the light intensity is large, for small Q₁, there is a high probability that the sensor will be saturated, i.e., all the pixel values will be “1”. So when the light intensity is large, we need larger thresholds Q₁. When the light intensity is small, for large Q₁, there is a high probability that the sensor will have all “0” output, which makes the sensor not sensitive to low light intensity. So when the light intensity is small, we need small Q₁. If Q₁>2, there exists an optimal (NM)_(opt) for a given γ_(j). The NM larger than (NM)_(opt) will have worse performance.

FIG. 10 shows an example of a 1-D synthesized signal γ. FIG. 11 shows an example of an upsampled and low-pass filtered signal {tilde over (λ)}, using an oversampling factor N=100, and an exposure time τ=1. FIG. 12 shows an example of a binary sequence generated by the camera/image sensor with threshold Q₁=1, an oversampling factor N=100, and total exposure time τ=1. FIG. 13 shows an example of a 1-D synthesized signal γ, as well as the estimated signal {circumflex over (γ)}, using a threshold Q₁=1, an oversampling factor N=100, a number of exposures M=1, and an exposure time of τ=1. FIG. 14 shows an example of a 1-D synthesized signal γ, as well as the estimated signal {circumflex over (γ)}, using a threshold Q₁=1, an oversampling factor N=20000, a number of exposures M=1 and an exposure time τ=1.

The above described methods may be performed by any suitable means capable of performing the operations, such as various hardware and/or software component(s) in a still or video camera, in other image acquisition devices, or in any image processing apparatus, including computer and workstation with suitable image processing applications.

The above described methods and apparatus can be used in consumer image acquisition systems, such as still and video cameras, mobile phone with camera, webcams, etc. Those methods and apparatus are in particular useful for the acquisition of still and video images with a high dynamic range, such as but not limited to high dynamic range photography, low light acquisition (for astronomy or night images), DNA image analysis, chromatography etc.

The various equations and processing steps described in the present application may be performed by a software executed by a general purpose processor or by a digital signal processor (DSP), by an application specific integrated circuit (ASIC), by a field programmable gate array signal (FPGA), by discrete components or any combination thereof. The apparatus may be an image acquisition apparatus, such as a camera comprising a lens, an image processing apparatus in the camera or as a separate equipment (such as a separate computer), or a combination between the two, such as a camera used in combination or sequentially with a computer for acquiring and processing still or video images.

Any steps of a method according to the present application may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. Thus, the invention also relates to a computer program product for performing the operations presented herein. If implemented in software, the functions described may be stored as one or more instructions on a computer-readable medium. Some examples of storage media that may be used include random access memory (RAM), read only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, other optical disks, or any available media that can be accessed by a computer, a camera or an image acquisition apparatus.

APPENDIX

Gradient of L(γ)

By applying the chain rule, we can show that:

$\begin{matrix} {{{\,_{\nabla}\mathcal{L}}(\gamma)} = \frac{\partial{{\,\mathcal{L}}(\gamma)}}{\partial\gamma}} \\ {= {\frac{{\partial\overset{\sim}{\lambda}}\,}{\partial\gamma}\frac{\partial{\mathcal{L}(\gamma)}}{\partial\overset{\sim}{\lambda}}}} \\ {= {G^{T}\frac{\partial{\sum\limits_{k = 1}^{K}\; {\mathcal{L}_{k}(\gamma)}}}{\partial\overset{\sim}{\lambda}}}} \\ {= {G^{T}\left\lbrack {\frac{\partial{\mathcal{L}_{1}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{1}},\frac{\partial{\mathcal{L}_{2}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{2}},\ldots \mspace{14mu},\frac{\partial{\mathcal{L}_{K}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{K}}} \right\rbrack}^{T}} \end{matrix}$

The Hessian Matrix of L(γ)

The Hessian matrix of L(γ) is H=G^(T) AG, where

$A = {\begin{pmatrix} \frac{\partial^{2}{\mathcal{L}_{1}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{1}} & 0 & \ldots & 0 \\ 0 & \frac{\partial^{2}{\mathcal{L}_{2}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{2}} & \ldots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots & \frac{\partial^{2}{\mathcal{L}_{K}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{K}} \end{pmatrix}.}$

Proof:

According to the chain rule,

$\begin{matrix} {H = {\frac{\partial}{\gamma}\left( \frac{\partial{\mathcal{L}(\gamma)}}{\partial\gamma} \right)}} \\ {= {\frac{{\partial\overset{\sim}{\lambda}}\,}{\partial\gamma}\frac{\partial}{\overset{\sim}{\lambda}}\left( \frac{\partial{\mathcal{L}(\gamma)}}{\partial\gamma} \right)}} \\ {= {G^{T}\frac{\partial\left\lbrack {\frac{\partial{\mathcal{L}_{1}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{1}},\frac{\partial{\mathcal{L}_{2}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{2}},\ldots \mspace{14mu},\frac{\partial{\mathcal{L}_{K}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{K}}} \right\rbrack^{T}}{{\partial\overset{\sim}{\lambda}}\,}G}} \\ {= {{G^{T}\begin{pmatrix} \frac{\partial^{2}{\mathcal{L}_{1}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{1}} & 0 & \ldots & 0 \\ 0 & \frac{\partial^{2}{\mathcal{L}_{2}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{2}} & \ldots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots & \frac{\partial^{2}{\mathcal{L}_{K}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{K}} \end{pmatrix}}G}} \end{matrix}$

Convexity of L(γ)

To prove L(γ) is a convex function, it is sufficient to show that the Hessian matrix of L(γ), H is positive semidefinite. Looking at the Hessian matrix, we need to prove that

${\frac{\partial^{2}{L_{k}(y)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} \geq 0},{k = 1},2,{\ldots \mspace{14mu} {K.}}$

Let

${P_{Q}(x)} = \left\{ {{\begin{matrix} {{\sum\limits_{q = 0}^{Q - 1}\; \frac{x^{q}}{q!}},} & {Q \geq 1} \\ {0,} & {{Q \leq 0},} \end{matrix}{R_{Q}(x)}} = \left\{ \begin{matrix} {\frac{x^{Q - 1}}{\left( {Q - 1} \right)!},} & {Q \geq 1} \\ {0,} & {{Q \leq 0},} \end{matrix} \right.} \right.$

To simply the notation, we use P_(Q) to indicate the function P_(Q)(x). The first derivative of P_(Q) is, P′_(Q)=P_(Q−1). The first derivative of R′_(Q)=R_(Q−1). We also have the equation that P_(Q)−P_(Q−1)=R_(Q), and

${P_{Q}(x)} = {\sum\limits_{q = {- \infty}}^{Q}\; {{R_{q}(x)}.}}$

When B_(k)=0,

$\begin{matrix} \begin{matrix} {\frac{\partial{\mathcal{L}_{k}(y)}}{\partial{\overset{\sim}{\lambda}}_{k}} = \frac{- {\partial{\ln \left( {P\left( {{B_{k} = 0};{\overset{\sim}{\lambda}}_{k}} \right)} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= \frac{- {\partial{\ln \left( {^{- {\overset{\sim}{\lambda}}_{k}}{P_{Q_{1}}\left( {\overset{\sim}{\lambda}}_{k} \right)}} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= {1 - \frac{P_{Q_{1}}^{\prime}}{P_{Q_{1}}}}} \\ {= {1 - \frac{P_{Q_{1} - 1}}{P_{Q_{1}}}}} \\ {= \frac{R_{Q_{1}}}{P_{Q_{1}}}} \end{matrix} & \; \\ \begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{\partial}{\partial{\overset{\sim}{\lambda}}_{k}}\left( \frac{R_{Q_{1}}}{P_{Q_{1}}} \right)}} \\ {= \frac{{R_{Q_{1}}^{\prime}P_{Q_{1}}} - {R_{Q_{1}}P_{Q_{1}}^{\prime}}}{P_{Q_{1}}^{2}}} \\ {= \frac{{R_{Q_{1} - 1}P_{Q_{1}}} - {R_{Q_{1}}P_{Q_{1} - 1}}}{P_{Q_{1}}^{2}}} \end{matrix} & \; \end{matrix}$

If Q₁=1, then, R_(Q) ₁ ⁻¹=0, P_(Q) ₁ =1, R_(Q) ₁ =1, P_(Q) ₁ ⁻¹=0, then

${\frac{\partial^{2}{L_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {0 \geq 0}},$

If Q₁=2, then, R_(Q) ₁ ⁻¹=1, P_(Q) ₁ =1+{tilde over (λ)}_(k), R_(Q) ₁ ={tilde over (λ)}_(k), P_(Q) ₁ ⁻¹=1, then

$\frac{\partial^{2}{L_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{1 + {\overset{\sim}{\lambda}}_{k} - {\overset{\sim}{\lambda}}_{k}}{P_{Q_{1}}^{2}} = {\frac{1}{P_{Q_{1}}^{2}} \geq 0.}}$

If Q₁≧3,

$\begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = \frac{{R_{Q_{1} - 1}P_{Q_{1}}} - {R_{Q_{1}}P_{Q_{1} - 1}}}{P_{Q_{1}}^{2}}} \\ {= {\frac{R_{Q_{1} - 1}}{P_{Q_{1}}^{2}}\left( {P_{Q_{1}} - {\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{1} - 1}P_{Q_{1} - 1}}} \right)}} \\ {= {\frac{R_{Q_{1} - 1}}{P_{Q_{1}}^{2}}\left( {{\sum\limits_{q = 0}^{Q_{1} - 1}\; \frac{{\overset{\sim}{\lambda}}_{k}^{q}}{q!}} - {\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{1} - 1}{\sum\limits_{q = 0}^{Q_{1} - 2}\; \frac{{\overset{\sim}{\lambda}}_{k}^{q}}{q!}}}} \right)}} \\ {= {\frac{R_{Q_{1} - 1}}{P_{Q_{1}}^{2}}\left( {{\sum\limits_{q = 0}^{Q_{1} - 1}\; \frac{{\overset{\sim}{\lambda}}_{k}^{q}}{q!}} - {\frac{1}{Q_{1} - 1}{\sum\limits_{q = 0}^{Q_{1} - 2}\; \frac{{\overset{\sim}{\lambda}}_{k}^{q + 1}}{q!}}}} \right)}} \\ {= {\frac{R_{Q_{1} - 1}}{P_{Q_{1}}^{2}}\left( {{\sum\limits_{q = 0}^{Q_{1} - 1}\; \frac{{\overset{\sim}{\lambda}}_{k}^{q}}{q!}} - {\frac{1}{Q_{1} - 1}{\sum\limits_{q = 0}^{Q_{1} - 1}\; \frac{q{\overset{\sim}{\lambda}}_{k}^{q}}{q!}}}} \right)}} \\ {= {{\frac{R_{Q_{1} - 1}}{P_{Q_{1}}^{2}}{\sum\limits_{q = 0}^{Q_{1} - 1}{\left( {1 - \frac{q}{Q_{1} - 1}} \right)\frac{{\overset{\sim}{\lambda}}_{k}^{q}}{q!}}}} \geq 0}} \end{matrix}$

and we also have R_(Q−1)P_(Q)−R_(Q)P_(Q−1)≧0, when (2). When 1≦B_(k)=l≦L−2,

$\begin{matrix} \begin{matrix} {\frac{\partial{\mathcal{L}_{k}(y)}}{\partial{\overset{\sim}{\lambda}}_{k}} = \frac{- {\partial{\ln \left( {P\left( {{B_{k} = l};{\overset{\sim}{\lambda}}_{k}} \right)} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= \frac{- {\partial{\ln \left( {{^{- {\overset{\sim}{\lambda}}_{k}}{P_{Q_{l + 1}}\left( {\overset{\sim}{\lambda}}_{k} \right)}} - {P_{Q_{l}}\left( {\overset{\sim}{\lambda}}_{k} \right)}} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= {1 - \frac{P_{Q_{l + 1}}^{\prime} - P_{Q_{l}}^{\prime}}{P_{Q_{l + 1}} - P_{Q_{l}}}}} \\ {= {1 - \frac{P_{Q_{l + 1} - 1} - P_{Q_{l} - 1}}{P_{Q_{l + 1}} - P_{Q_{l}}}}} \\ {= \frac{R_{Q_{l + 1}} - R_{Q_{l}}}{P_{Q_{l + 1}} - P_{Q_{l}}}} \end{matrix} & \; \\ \begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{\partial}{\partial{\overset{\sim}{\lambda}}_{k}}\left( \frac{R_{Q_{l + 1}} - R_{Q_{l}}}{P_{Q_{l + 1}} - P_{Q_{l}}} \right)}} \\ {= \frac{\begin{matrix} {{\left( {R_{Q_{l + 1}}^{\prime} - R_{Q_{l}}^{\prime}} \right)\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)} -} \\ {\left( {R_{Q_{l + 1}} - R_{Q_{l}}} \right)\left( {P_{Q_{l + 1}}^{\prime} - P_{Q_{l}}^{\prime}} \right)} \end{matrix}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}}} \\ {= {\frac{\begin{matrix} {{\left( {R_{Q_{l + 1} - 1} - R_{Q_{l} - 1}} \right)\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)} -} \\ {\left( {R_{Q_{l + 1}} - R_{Q_{l}}} \right)\left( {P_{Q_{l + 1} - 1} - P_{Q_{l} - 1}} \right)} \end{matrix}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}}.}} \end{matrix} & \; \end{matrix}$

Here Q_(l+1) should be greater than Q_(l). If Q_(l)=1, Q_(l+1)=2, then, R_(Q) _(l+1) ⁻¹=R₁=1, R_(Q) _(l) ⁻¹=R₀=0, P_(Q) _(l+1) =P₂₌₁+{tilde over (λ)}_(k), P_(Q) _(l) =P₁=1

$,{R_{Q_{l + 1}} = {\overset{\sim}{\lambda}}_{k}},{R_{Q_{l}} = {R_{1} = 1}},{P_{Q_{l + 1} - 1} = {P_{1} = 1}},{P_{Q_{l} - 1} = 0},\begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = \frac{\begin{matrix} {{\left( {R_{Q_{l + 1} - 1} - R_{Q_{l} - 1}} \right)\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)} -} \\ {\left( {R_{Q_{l + 1}} - R_{Q_{l}}} \right)\left( {P_{Q_{l + 1} - 1} - P_{Q_{l} - 1}} \right)} \end{matrix}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}}} \\ {= {\frac{1}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}} \geq 0}} \end{matrix}$

If Q_(l)=1, Q_(l+1)≧3, then, R_(Q) _(l) ⁻¹=R₀=0, P_(Q) _(l) =P₁=1, R_(Q) _(l) =R₁=1, P_(Q) _(l) ⁻¹=0,

$\begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = \frac{{R_{Q_{l + 1} - 1}\left( {P_{Q_{l + 1}} - 1} \right)} - {\left( {R_{Q_{l + 1}} - 1} \right)P_{Q_{l + 1} - 1}}}{\left( {P_{Q_{l + 1}} - 1} \right)^{2}}} \\ {= \frac{{R_{Q_{l + 1} - 1}P_{Q_{l + 1}}} - {R_{Q_{l + 1}}P_{Q_{l + 1} - 1}} + P_{Q_{l + 1} - 2}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}}} \end{matrix}$

From equation (2), we know that R_(Q) _(l+1) ⁻¹P_(Q) _(l+1) −R_(Q) _(l+1) P_(Q) _(l+1) ⁻¹≧0, also P_(Q) _(l+1) ⁻²≧0, so

$\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{{R_{Q_{l + 1} - 1}P_{Q_{l + 1}}} - {R_{Q_{l + 1}}P_{Q_{l + 1} - 1}} + P_{Q_{l + 1} - 2}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}} \geq 0}$

If Q_(l)≧2, then Q_(l+1)≧3,

${{\left( {R_{Q_{l + 1} - 1} - R_{Q_{l - 1}}} \right)\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)} - {\left( {R_{Q_{l + 1}} - R_{Q_{l}}} \right)\left( {P_{Q_{l + 1} - 1} - P_{Q_{l} - 1}} \right)}} = {{{R_{Q_{l + 1} - 1}{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}\; R_{q}}} - {R_{Q_{l} - 1}{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}}} - {R_{Q_{l + 1}}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{q}}} + {R_{Q_{l}}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{q}}}} = {{R_{Q_{l + 1} - 1}\left( {{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}} - {\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{l + 1} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{q}}}} \right)} + {R_{Q_{l} - 1}\left( {{\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{l} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{Q}}} - {\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}}} \right)}}}$ $\begin{matrix} {{{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}} - {\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{l + 1} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{q}}}} = {{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}} - {\frac{1}{Q_{l + 1} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}\frac{{\overset{\sim}{\lambda}}_{k}^{q}}{\left( {q - 1} \right)!}}}}} \\ {= {{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}} - {\frac{1}{Q_{l + 1} - 1}{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}\frac{\left( {q - 1} \right){\overset{\sim}{\lambda}}_{k}^{q - 1}}{\left( {q - 1} \right)!}}}}} \\ {= {{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}{\left( \frac{Q_{l + 1} - q}{Q_{l + 1} - 1} \right)R_{q}}} \geq 0}} \end{matrix}$ $\begin{matrix} {{{\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{l} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}R_{q}}} - \sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}} = {{\frac{1}{Q_{l} - 1}{\sum\limits_{q = Q_{l}}^{Q_{l + 1} - 1}\frac{{\overset{\sim}{\lambda}}_{k}^{q}}{\left( {q - 1} \right)!}}} - {\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}}}} \\ {= {{\frac{1}{Q_{l} - 1}{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}{\left( {q - 1} \right)\frac{{\overset{\sim}{\lambda}}_{k}^{q - 1}}{\left( {q - 1} \right)!}}}} - {\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}R_{q}}}} \\ {= {{\sum\limits_{q = {Q_{l} + 1}}^{Q_{l + 1}}{\left( \frac{q - Q_{l}}{Q_{l} - 1} \right)R_{q}}} \geq 0}} \end{matrix}$ (R _(Q) _(l+1) ⁻¹ −R _(Q) _(l) ⁻¹)(P _(Q) _(l+1) −P _(Q) _(l) )−(R _(Q) _(l+1) −R _(Q) _(l) )(P _(Q) _(l+1) ⁻¹ −P _(Q) _(l) ⁻¹)≧0

So, Then,

$\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{\begin{matrix} {{\left( {R_{Q_{l + 1} - 1} - R_{Q_{l} - 1}} \right)\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)} -} \\ {\left( {R_{Q_{l + 1}} - R_{Q_{l}}} \right)\left( {P_{Q_{l + 1} - 1} + P_{Q_{l} - 1}} \right)} \end{matrix}}{\left( {P_{Q_{l + 1}} - P_{Q_{l}}} \right)^{2}} \geq 0}$ ${\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} \geq 0},$

Therefore, when 1≦B_(k)=l≦L−2,

When B_(k)=L−1,

$\begin{matrix} {\frac{\partial{\mathcal{L}_{k}(\gamma)}}{\partial{\overset{\sim}{\lambda}}_{k}} = \frac{- {\partial{\ln \left( {{\mathbb{P}}\left( {{B_{k} = {L - 1}};{\overset{\sim}{\lambda}}_{k}} \right)} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= \frac{- {\partial{\ln \left( {1 - {^{- {\overset{\sim}{\lambda}}_{k}}{P_{Q_{L - 1}}\left( {\overset{\sim}{\lambda}}_{k} \right)}}} \right)}}}{\partial{\overset{\sim}{\lambda}}_{k}}} \\ {= {1 - \frac{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}^{\prime}}{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}}}} \\ {= {1 - \frac{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1} - 1}}{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}}}} \\ {= {- \frac{R_{Q_{L - 1}}}{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}}}} \end{matrix}$ $\begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{\partial}{\partial{\overset{\sim}{\lambda}}_{k}}\left( {- \frac{R_{Q_{L - 1}}}{^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}}} \right)}} \\ {= {- \frac{{R_{Q_{L - 1}}^{\prime}\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)} - {R_{Q_{L - 1}}\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}^{\prime}} \right)}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}} \\ {= {- \frac{{R_{Q_{L - 1} - 1}\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)} - {R_{Q_{L - 1}}\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1} - 1}} \right)}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}} \end{matrix}$

If Q_(L−1)=1, then, R_(Q) _(L−1) ⁻¹=0, P_(Q) _(L−1) =1, R_(Q) _(L−1) =1, P_(Q) _(L−1) ⁻¹=0, thus,

$\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {\frac{^{{\overset{\sim}{\lambda}}_{k}}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}} \geq 0}$

If Q_(L−1)≧2,

$\begin{matrix} {\frac{\partial^{2}{\mathcal{L}_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} = {{- \frac{R_{Q_{L - 1} - 1}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}\left( {{\sum\limits_{q = {Q_{L - 1} + 1}}^{+ \infty}\; R_{q}} - {\frac{{\overset{\sim}{\lambda}}_{k}}{Q_{L - 1} - 1}{\sum\limits_{q = Q_{L - 1}}^{+ \infty}R_{q}}}} \right)}} \\ {= {{- \frac{R_{Q_{L - 1} - 1}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}\left( {{\sum\limits_{q = {Q_{L - 1} + 1}}^{+ \infty}\; R_{q}} - {\frac{1}{Q_{L - 1} - 1}{\sum\limits_{q = Q_{L - 1}}^{+ \infty}\frac{{\overset{\sim}{\lambda}}_{k}^{q}}{\left( {q - 1} \right)!}}}} \right)}} \\ {= {{- \frac{R_{Q_{L - 1} - 1}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}\left( {{\sum\limits_{q = {Q_{L - 1} + 1}}^{+ \infty}\; R_{q}} - {\frac{1}{Q_{L - 1} - 1}{\sum\limits_{q = {Q_{L - 1} + 1}}^{+ \infty}\frac{\left( {q - 1} \right){\overset{\sim}{\lambda}}_{k}^{q - 1}}{\left( {q - 1} \right)!}}}} \right)}} \\ {= {{{- \frac{R_{Q_{L - 1} - 1}}{\left( {^{{\overset{\sim}{\lambda}}_{k}} - P_{Q_{L - 1}}} \right)^{2}}}{\sum\limits_{q = {Q_{L - 1} + 1}}^{+ \infty}{\frac{Q_{L - 1} - q}{Q_{L - 1} - 1}R_{q}}}} \geq 0}} \end{matrix}$

So, when

${B_{k} = {L - 1}},{\frac{\partial^{2}{L_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} \geq 0.}$

From the above, we can make the conclusion that for any

$B_{k},{\frac{\partial^{2}{L_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} \geq 0.}$

So the Hessian matrix of L(γ) is positive semidefinite, it is a convex function.

Gradient of L(γ)

The gradient of L(γ) is

${\nabla{\overset{\sim}{\mathcal{L}}(\gamma)}} = {\frac{1}{M}G^{T}{\sum\limits_{m = 1}^{M}\; {\left\lbrack {\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 1}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{1}},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 2}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{2}},\ldots \mspace{14mu},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{mK}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}}} \right\rbrack^{T}.}}}$

Proof:

According to the chain rule,

$\begin{matrix} {{\nabla{\overset{\sim}{\mathcal{L}}(\gamma)}} = \frac{\partial{\overset{\sim}{\mathcal{L}}(\gamma)}}{\partial\gamma}} \\ {= {\frac{\partial\overset{\sim}{\lambda}}{\partial\gamma}\frac{\partial\overset{\sim}{\overset{\sim}{\lambda}}}{\partial\overset{\sim}{\lambda}}\frac{\partial{\overset{\sim}{\mathcal{L}}(\gamma)}}{\partial\overset{\sim}{\overset{\sim}{\lambda}}}}} \\ {= {\frac{1}{M}G^{T}\frac{\partial{\sum\limits_{m = 1}^{M}\; {\sum\limits_{k = 1}^{K}\; {{\overset{\sim}{\mathcal{L}}}_{mk}(\gamma)}}}}{\partial\overset{\sim}{\overset{\sim}{\lambda}}}}} \\ {= {\frac{1}{M}G^{T}{\sum\limits_{m = 1}^{M}{\left\lbrack {\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 1}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{1}},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 2}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{2}},\ldots \mspace{14mu},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{mK}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}}} \right\rbrack^{T}.}}}} \end{matrix}$

Hessian Matrix of L(γ)

The Hessian matrix of

${{{\mathcal{L}(\gamma)}\mspace{14mu} {is}\mspace{14mu} \overset{\sim}{H}} = {\frac{1}{M^{2}}G^{T}\overset{\sim}{A}G}},$

where,

$\overset{\sim}{A} = {\begin{pmatrix} {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{m\; 1}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{1}}} & 0 & \cdots & 0 \\ 0 & {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{m\; 2}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{2}}} & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{mK}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}}} \end{pmatrix}.}$

Proof:

According to the chain rule,

$\begin{matrix} {\overset{\sim}{H} = {\frac{\partial}{\gamma}\left( \frac{\partial{\overset{\sim}{\mathcal{L}}(\gamma)}}{\partial\gamma} \right)}} \\ {= {\frac{\partial\overset{\sim}{\lambda}}{\partial\gamma}\frac{\partial\overset{\sim}{\overset{\sim}{\lambda}}}{\partial\gamma}\frac{\partial}{\overset{\sim}{\overset{\sim}{\lambda}}}\left( \frac{\partial{\mathcal{L}(\gamma)}}{\partial\gamma} \right)}} \\ {= {\frac{1}{M^{2}}G^{T}\frac{\partial{\sum\limits_{m = 1}^{M}\left\lbrack {\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 1}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{1}},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{m\; 2}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{2}},\ldots \mspace{14mu},\frac{\partial{{\overset{\sim}{\mathcal{L}}}_{mK}(\gamma)}}{\partial{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}}} \right\rbrack^{T}}}{\partial\overset{\sim}{\overset{\sim}{\lambda}}}G}} \\ {= {\frac{1}{M^{2}}{G^{T}\begin{pmatrix} {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{m\; 1}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{1}}} & 0 & \cdots & 0 \\ 0 & {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{m\; 2}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{2}}} & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & {\sum\limits_{m = 1}^{M}\frac{\partial^{2}{{\overset{\sim}{\mathcal{L}}}_{mK}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}}} \end{pmatrix}}G}} \end{matrix}$

Convexity of L(γ)

To prove L(γ) is a convex function, it is sufficient to show that the Hessian matrix of L(γ), {tilde over (H)} is positive semidefinite. Looking at the Hessian matrix, we need to prove that

${\frac{\partial^{2}{{\overset{\sim}{L}}_{mK}(\gamma)}}{\partial^{2}{\overset{\sim}{\overset{\sim}{\lambda}}}_{K}} \geq 0},$

m=1, 2, . . . , M, k=1, 2, . . . , K. This can be proved in the similar way as prove

${\frac{\partial^{2}{L_{k}(\gamma)}}{\partial^{2}{\overset{\sim}{\lambda}}_{k}} \geq 0},$

k=1, 2, . . . , K. 

1. A method for image acquisition and conversion comprising low-pass filtering an image by an acquisition lens, producing from said low-pass filtered image an up˜sampled image with a first resolution with an up-sampling factor using a image sensor, converting said up-sampled image into a multi-level image with a second resolution lower than said first resolution with an image processing circuit wherein said converting depends on said low-pass filtering of said lens and on said up-sampling factor.
 2. The method of claim 1, wherein said up-sampled image is produced with a gigapixel image sensor that outputs a binary up-sampled image.
 3. The method of claim 1, wherein said converting comprises computing an estimate ({circumflex over (γ)}) of the light intensity field based on said low-pass filtered image, and wherein a maximum likelihood estimator (MLE) is used to compute said estimate.
 4. The method of claim 3, comprising using a Newton method or modified Newton method for computing said maximum likelihood estimator.
 5. The method of claim 3, wherein one image is reconstructed from a plurality of successive exposures resulting in temporal oversampling.
 6. The method of claim 3, wherein a polyphase representation of the signals and/or operators is used in order to increase the computing speed.
 7. The method of claim 3, comprising performing a conjugate gradients method.
 8. The method of one of claim 1, comprising using pixels with various light sensitivity thresholds on the same image sensor.
 9. An image acquisition apparatus comprising a lens having a low-pass filtering function, an image sensor with a first resolution producing an up-sampled image with an up-sampling factor, an image processing circuit for converting said image into a multi-level image with a second resolution lower than said first resolution wherein said image processing circuit depends on said low-pass filtering function of said lens and on said up-sampling factor.
 10. The image acquisition apparatus of claim 9, wherein said image sensor is a binary sensor arranged for producing a binary up-sampled image.
 11. The image acquisition apparatus of claim 9, wherein said image processing circuit is arranged for computing an estimate of the light intensity field based on said low-pass filtered image, said apparatus comprising a maximum likelihood estimator (MLE) arranged for computing said estimate.
 12. The image acquisition apparatus of claim 11, comprising a filter bank arranged for computing the gradient and the multiplication of a vector and Hessian matrix of a negative log-likelihood function.
 13. The image acquisition apparatus of claim 9, arranged for reconstructing one image from a plurality of successive exposures.
 14. The image acquisition apparatus of claim 11, wherein said apparatus is a still or video camera.
 15. The image acquisition apparatus of claim 9, wherein said image sensor comprises a pattern of pixels with various light sensitivity thresholds.
 16. A computer-program product for signal processing, comprising a computer readable medium comprising instructions executable to process an up-sampled image with a first resolution produced by an image sensor that up-samples an image acquires by a lens with an up-sampling factor, for converting said image into a multi-level image with a second resolution lower than said first resolution wherein said converting step depends on a low-pass filtering of said lens and on said up-sampling factor.
 17. The computer-program product of claim 16, wherein said image processing circuit implements a maximum likelihood estimation (MLE) method.
 18. The computer-program product of claim 17, wherein at least two exposures are provided for each image acquired.
 19. The computer-program product of claim 17, wherein said instructions are executable by a processor in a camera.
 20. A method for reconstructing an image from measurements taken by an image sensor, comprising: filtering with a lens and up-sampling with an image sensor a light intensity field, in order to obtain oversampled light intensity values, processing said oversampled light intensity values in order to generate the reconstructed image using maximum likelihood estimation.
 21. The method of claim 20, wherein said processing exploits the convexity of the negative log-likelihood function of said maximum likelihood estimation when the threshold of said image sensor is normalized to one, in order to use a fast algorithm for solving convex optimization problem.
 22. The method of claim 20, wherein said image sensor is a binary image sensor that produces a binary image.
 23. A system for reconstructing an image from binary measurements comprising: a first low-pass filter and an up-sampling image sensor for up-sampling with an up-sampling factor a sampled light intensity factor values, in order to obtain oversampled light intensity values, a circuit for processing said oversampled light intensity values in order to generate the reconstructed image using maximum likelihood estimation. 